
"""simple docstring"""






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def __magic_name__	(				_lowerCamelCase:		List[Any],					_lowerCamelCase:		Dict=1			)					->						Tuple:

	'''simple docstring'''




	if n_shave_prefix_segments >= 0:
		return ".".join(path.split('''.'''			)[n_shave_prefix_segments:]			)
	else:
		return ".".join(path.split('''.'''			)[:n_shave_prefix_segments]			)




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Optional[int]=0			)					->						Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase												=  []
	for old_item in old_list:
		lowerCAmelCase												=  old_item.replace('''in_layers.0''',					'''norm1'''			)
		lowerCAmelCase												=  new_item.replace('''in_layers.2''',					'''conv1'''			)

		lowerCAmelCase												=  new_item.replace('''out_layers.0''',					'''norm2'''			)
		lowerCAmelCase												=  new_item.replace('''out_layers.3''',					'''conv2'''			)

		lowerCAmelCase												=  new_item.replace('''emb_layers.1''',					'''time_emb_proj'''			)
		lowerCAmelCase												=  new_item.replace('''skip_connection''',					'''conv_shortcut'''			)

		lowerCAmelCase												=  shave_segments(_lowerCamelCase,					n_shave_prefix_segments=_lowerCamelCase			)

		mapping.append({'''old''': old_item, '''new''': new_item}			)

	return mapping




def __magic_name__	(				_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Optional[Any]=0			)					->						Any:

	'''simple docstring'''




	lowerCAmelCase												=  []
	for old_item in old_list:
		lowerCAmelCase												=  old_item

		lowerCAmelCase												=  new_item.replace('''norm.weight''',					'''group_norm.weight'''			)
		lowerCAmelCase												=  new_item.replace('''norm.bias''',					'''group_norm.bias'''			)

		lowerCAmelCase												=  new_item.replace('''proj_out.weight''',					'''proj_attn.weight'''			)
		lowerCAmelCase												=  new_item.replace('''proj_out.bias''',					'''proj_attn.bias'''			)

		lowerCAmelCase												=  shave_segments(_lowerCamelCase,					n_shave_prefix_segments=_lowerCamelCase			)

		mapping.append({'''old''': old_item, '''new''': new_item}			)

	return mapping




def __magic_name__	(				_lowerCamelCase:		List[Any],					_lowerCamelCase:		int,					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Tuple=None,					_lowerCamelCase:		int=None,					_lowerCamelCase:		Union[str, Any]=None			)					->						Union[str, Any]:

	'''simple docstring'''




	assert isinstance(_lowerCamelCase,					_lowerCamelCase			), "Paths should be a list of dicts containing 'old' and 'new' keys."

	# Splits the attention layers into three variables.
	if attention_paths_to_split is not None:
		for path, path_map in attention_paths_to_split.items():
			lowerCAmelCase												=  old_checkpoint[path]
			lowerCAmelCase												=  old_tensor.shape[0] // 3

			lowerCAmelCase												=  (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

			lowerCAmelCase												=  old_tensor.shape[0] // config['''num_head_channels'''] // 3

			lowerCAmelCase												=  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
			lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  old_tensor.split(channels // num_heads,					dim=1			)

			lowerCAmelCase												=  query.reshape(_lowerCamelCase			)
			lowerCAmelCase												=  key.reshape(_lowerCamelCase			)
			lowerCAmelCase												=  value.reshape(_lowerCamelCase			)

	for path in paths:
		lowerCAmelCase												=  path['''new''']

		# These have already been assigned
		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		# Global renaming happens here
		lowerCAmelCase												=  new_path.replace('''middle_block.0''',					'''mid_block.resnets.0'''			)
		lowerCAmelCase												=  new_path.replace('''middle_block.1''',					'''mid_block.attentions.0'''			)
		lowerCAmelCase												=  new_path.replace('''middle_block.2''',					'''mid_block.resnets.1'''			)

		if additional_replacements is not None:
			for replacement in additional_replacements:
				lowerCAmelCase												=  new_path.replace(replacement['''old'''],					replacement['''new''']			)

        # proj_attn.weight has to be converted from conv 1D to linear
		if "proj_attn.weight" in new_path:
			lowerCAmelCase												=  old_checkpoint[path['''old''']][:, :, 0]
		else:
			lowerCAmelCase												=  old_checkpoint[path['''old''']]




def __magic_name__	(				_lowerCamelCase:		int,					_lowerCamelCase:		Union[str, Any]			)					->						Optional[int]:

	'''simple docstring'''




	lowerCAmelCase												=  {}

	lowerCAmelCase												=  checkpoint['''time_embed.0.weight''']
	lowerCAmelCase												=  checkpoint['''time_embed.0.bias''']
	lowerCAmelCase												=  checkpoint['''time_embed.2.weight''']
	lowerCAmelCase												=  checkpoint['''time_embed.2.bias''']

	lowerCAmelCase												=  checkpoint['''input_blocks.0.0.weight''']
	lowerCAmelCase												=  checkpoint['''input_blocks.0.0.bias''']

	lowerCAmelCase												=  checkpoint['''out.0.weight''']
	lowerCAmelCase												=  checkpoint['''out.0.bias''']
	lowerCAmelCase												=  checkpoint['''out.2.weight''']
	lowerCAmelCase												=  checkpoint['''out.2.bias''']

	# Retrieves the keys for the input blocks only
	lowerCAmelCase												=  len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''input_blocks''' in layer}			)
	lowerCAmelCase												=  {
	    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
	    for layer_id in range(_lowerCamelCase			)
	}

	# Retrieves the keys for the middle blocks only
	lowerCAmelCase												=  len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''middle_block''' in layer}			)
	lowerCAmelCase												=  {
	    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
	    for layer_id in range(_lowerCamelCase			)
	}

	# Retrieves the keys for the output blocks only
	lowerCAmelCase												=  len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''output_blocks''' in layer}			)
	lowerCAmelCase												=  {
	    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
	    for layer_id in range(_lowerCamelCase			)
	}

	for i in range(1,					_lowerCamelCase			):
		lowerCAmelCase												=  (i - 1) // (config['''num_res_blocks'''] + 1)
		lowerCAmelCase												=  (i - 1) % (config['''num_res_blocks'''] + 1)

		lowerCAmelCase												=  [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
		lowerCAmelCase												=  [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

		if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
			lowerCAmelCase												=  checkpoint[
			    F"""input_blocks.{i}.0.op.weight"""
			]
			lowerCAmelCase												=  checkpoint[
			    F"""input_blocks.{i}.0.op.bias"""
			]
			continue

		lowerCAmelCase												=  renew_resnet_paths(_lowerCamelCase			)
		lowerCAmelCase												=  {'''old''': F"""input_blocks.{i}.0""", '''new''': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
		lowerCAmelCase												=  {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
		assign_to_checkpoint(
		    _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					additional_replacements=[meta_path, resnet_op],					config=_lowerCamelCase			)

		if len(_lowerCamelCase			):
			lowerCAmelCase												=  renew_attention_paths(_lowerCamelCase			)
			lowerCAmelCase												=  {
			    '''old''': F"""input_blocks.{i}.1""",
			    '''new''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
			}
			lowerCAmelCase												=  {
			    F"""input_blocks.{i}.1.qkv.bias""": {
			        '''key''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
			        '''query''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
			        '''value''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
			    },
			    F"""input_blocks.{i}.1.qkv.weight""": {
			        '''key''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
			        '''query''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
			        '''value''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
			    },
			}
			assign_to_checkpoint(
			    _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					additional_replacements=[meta_path],					attention_paths_to_split=_lowerCamelCase,					config=_lowerCamelCase,					)

	lowerCAmelCase												=  middle_blocks[0]
	lowerCAmelCase												=  middle_blocks[1]
	lowerCAmelCase												=  middle_blocks[2]

	lowerCAmelCase												=  renew_resnet_paths(_lowerCamelCase			)
	assign_to_checkpoint(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					config=_lowerCamelCase			)

	lowerCAmelCase												=  renew_resnet_paths(_lowerCamelCase			)
	assign_to_checkpoint(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					config=_lowerCamelCase			)

	lowerCAmelCase												=  renew_attention_paths(_lowerCamelCase			)
	lowerCAmelCase												=  {
	    '''middle_block.1.qkv.bias''': {
	        '''key''': '''mid_block.attentions.0.key.bias''',
	        '''query''': '''mid_block.attentions.0.query.bias''',
	        '''value''': '''mid_block.attentions.0.value.bias''',
	    },
	    '''middle_block.1.qkv.weight''': {
	        '''key''': '''mid_block.attentions.0.key.weight''',
	        '''query''': '''mid_block.attentions.0.query.weight''',
	        '''value''': '''mid_block.attentions.0.value.weight''',
	    },
	}
	assign_to_checkpoint(
	    _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					attention_paths_to_split=_lowerCamelCase,					config=_lowerCamelCase			)

	for i in range(_lowerCamelCase			):
		lowerCAmelCase												=  i // (config['''num_res_blocks'''] + 1)
		lowerCAmelCase												=  i % (config['''num_res_blocks'''] + 1)
		lowerCAmelCase												=  [shave_segments(_lowerCamelCase,					2			) for name in output_blocks[i]]
		lowerCAmelCase												=  {}

		for layer in output_block_layers:
			lowerCAmelCase							,       lowerCAmelCase												=  layer.split('''.'''			)[0], shave_segments(_lowerCamelCase,					1			)
			if layer_id in output_block_list:
				output_block_list[layer_id].append(_lowerCamelCase			)
			else:
				lowerCAmelCase												=  [layer_name]

		if len(_lowerCamelCase			) > 1:
			lowerCAmelCase												=  [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
			lowerCAmelCase												=  [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

			lowerCAmelCase												=  renew_resnet_paths(_lowerCamelCase			)
			lowerCAmelCase												=  renew_resnet_paths(_lowerCamelCase			)

			lowerCAmelCase												=  {'''old''': F"""output_blocks.{i}.0""", '''new''': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
			assign_to_checkpoint(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					additional_replacements=[meta_path],					config=_lowerCamelCase			)

			if ["conv.weight", "conv.bias"] in output_block_list.values():
				lowerCAmelCase												=  list(output_block_list.values()			).index(['''conv.weight''', '''conv.bias''']			)
				lowerCAmelCase												=  checkpoint[
				    F"""output_blocks.{i}.{index}.conv.weight"""
				]
				lowerCAmelCase												=  checkpoint[
				    F"""output_blocks.{i}.{index}.conv.bias"""
				]

				# Clear attentions as they have been attributed above.
				if len(_lowerCamelCase			) == 2:
					lowerCAmelCase												=  []

			if len(_lowerCamelCase			):
				lowerCAmelCase												=  renew_attention_paths(_lowerCamelCase			)
				lowerCAmelCase												=  {
				    '''old''': F"""output_blocks.{i}.1""",
				    '''new''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
				}
				lowerCAmelCase												=  {
				    F"""output_blocks.{i}.1.qkv.bias""": {
				        '''key''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
				        '''query''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
				        '''value''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
				    },
				    F"""output_blocks.{i}.1.qkv.weight""": {
				        '''key''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
				        '''query''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
				        '''value''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
				    },
				}
				assign_to_checkpoint(
				    _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					additional_replacements=[meta_path],					attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions			) else None,					config=_lowerCamelCase,					)
		else:
			lowerCAmelCase												=  renew_resnet_paths(_lowerCamelCase,					n_shave_prefix_segments=1			)
			for path in resnet_0_paths:
				lowerCAmelCase												=  '''.'''.join(['''output_blocks''', str(_lowerCamelCase			), path['''old''']]			)
				lowerCAmelCase												=  '''.'''.join(['''up_blocks''', str(_lowerCamelCase			), '''resnets''', str(_lowerCamelCase			), path['''new''']]			)

				lowerCAmelCase												=  checkpoint[old_path]

	return new_checkpoint


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)

					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the architecture.""",
					)

					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

					UpperCAmelCase        =				parser.parse_args()

					UpperCAmelCase        =				torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										UpperCAmelCase        =				json.loads(f.read())

					UpperCAmelCase        =				convert_ldm_checkpoint(checkpoint, config)

					if "ldm" in config:
										del config["ldm"]

					UpperCAmelCase        =				UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										UpperCAmelCase        =				DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
										UpperCAmelCase        =				VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										UpperCAmelCase        =				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)




"""simple docstring"""






from itertools import count




def __magic_name__	(				_lowerCamelCase:		int = 50			)					->						int:

	'''simple docstring'''




	lowerCAmelCase												=  [1] * min_block_length

	for n in count(_lowerCamelCase			):
		fill_count_functions.append(1			)

		for block_length in range(_lowerCamelCase,					n + 1			):
			for block_start in range(n - block_length			):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1_000_000:
			break

	return n


if __name__ == "__main__":
					print(f"""{solution() = }""")


